the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Feb 2026
Spatiotemporal dynamics and drivers of bare soil albedo in European croplands
Abstract. Bare soil albedo plays a critical role in regulating surface energy balance and land-atmosphere interactions in agricultural systems, yet its spatiotemporal variability and controlling factors remain poorly quantified at the field scale across heterogeneous cropland landscapes. To address this, we investigate the spatial patterns and temporal dynamics of bare soil albedo in European croplands. We develop a method to reconstruct field-scale, spatiotemporally continuous bare soil albedo at a 5-day temporal resolution and 0.3 km spatial resolution using Sentinel-2 reflectance observations. Bare soil periods are identified by multiple spectral indices and the corresponding soil albedo values are derived for the period 2018–2020 using a novel machine learning framework. Two random forest models were employed to separately capture the long-term spatial structure and short-term temporal anomalies of bare soil albedo, allowing gaps caused by clouds, snow, and vegetation cover to be bridged. Model evaluation against independent site observations and existing products shows that the estimated bare soil albedo reproduces observed spatial gradients and seasonal variability across European croplands. Such variations in bare soil albedo are jointly controlled by soil properties, observation geometry and short-term soil moisture dynamics rather than by any single factor. Because these variations are of the same order of magnitude as radiation management solutions, soil radiative properties must be considered in their assessment. The resulting bare soil albedo offers a process-oriented basis for improving the representation of surface radiative properties and land-atmosphere coupling in agroecosystem and land surface models.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Biogeosciences. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.-
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Status: closed
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RC1: 'Comment on egusphere-2026-219', Anonymous Referee #1, 27 Mar 2026
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AC1: 'Reply on RC1', Ke Yu, 28 Jul 2026
[Response] We thank the reviewer for their positive assessment and are grateful for their encouraging comments on the quality, clarity, and relevance of our study. Please find our point-by-point responses to the reviewer’s comments below
Q1：I feel that the manuscript would benefit from a clearer and more explicit illustration of the relationships between soil moisture, soil texture, and albedo, which are mentioned in the abstract, objectives, and discussion (see also L391, L429, L455 and following).
[Response] Following the reviewer’s suggestion, we have revised Introduction and section 4.2 to clarify the relationship between soil moisture, soil texture, and albedo.
Lines 44-49: “Bare soil albedo is controlled by a combination of intrinsic soil properties and varying environmental conditions (He et al., 2019). Soil properties, including texture, mineral composition, roughness, organic matter content, and bulk density determine the optical characteristics of soil surface (Matthias et al., 2000, Cierniewski et al., 2018) and soil hydrological properties (Han et al., 2023). As a result, they affect soil albedo directly and indirectly (via modulating the relationship between albedo and water availability). Soil moisture and solar radiation geometry further modulate the short-term temporal variability of soil albedo by altering the amount and direction of the observed reflected solar radiation (Hapke, 1981; Gascoin et al., 2009, Badía et al., 2016).”
Lines 380-384: “The decomposition of bare soil albedo into spatial and temporal components provides a framework for interpreting its physical controls. Intrinsic soil properties affect bare soil albedo by establishing the optical characteristics of the soil surface and regulating soil-water interaction, while soil moisture dynamics and radiative geometry control the temporal fluctuations of the observed bare soil albedo.”
Lines 399-401: “The magnitude and duration of these responses depend on the soil water retention capacity (Post et al., 2000), resulting in different αbs responses to the same soil moisture change for different soil textures”.
Lines 408-413: “Soil properties explain much of the stable spatial structure onto which moisture and geometry act (Rizzo et al., 2023). For example, soil texture influences bare soil albedo through both direct and indirect pathways. Particle size, mineral composition directly influence soil optical properties (e.g., surface color and scattering characteristics), thereby establishing the inherent bare soil albedo. In addition, these properties related with texture regulate the soil hydraulic behaviour (Sadeghi et al 2018)”
Lines 430-432: “Overall, these controls are interacting components governing bare soil albedo rather than independent drivers. Among the intrinsic soil properties, soil texture plays an important role because it establishes the background optical characteristics of the soil surface and regulates soil-water interactions, while soil moisture and solar geometry determine the short-term variability of the observed αbs.”
Q2: As a 1st step I suggest moving Supplementary Figure 7 (linear relationship between site-level bare soil albedo and soil moisture) into the main text, as it directly supports some of the key arguments of the study.
Along the same lines, it would be useful to provide some basic contextual information for the validation sites, such as pedoclimatic conditions, crop rotations, and fallow periods. This would help the reader better understand the representativeness and variability of the dataset.[Response] Following the reviewer’s suggestion, we moved Fig S7 into section 3.3. We also added a summary table (Table S1) describing the validation sites, including their climatic conditions, dominant crop rotations, and typical length of fallow periods. Regarding fallow periods, we note that quantifying a fixed period for each site is difficult, as it varies among years depending on crop rotation and management practices (e.g., cover crops and tillage operations). Therefore, we provide the average fallow period duration in 2018-2020 estimated from the crop calendars reported for each site. Table S1 please see the Supplement.
Specific comments
Q3. L238 and following:
Could you add a notion of a gradient in your evaluation dataset? The dataset includes only eight sites, and it would strengthen the manuscript to better demonstrate that these sites cover a meaningful pedoclimatic gradient. I recommend to better describ how the selected sites represent variability in:
soil types (e.g. texture, possibly linked to soil color), climatic conditions (e.g. radiation, cloud cover), management practices (e.g. crop rotations, fallow periods), seasonal dynamics (as proxies for variations in radiation intensity, cloud cover, and soil moisture throughout the year; see also L271).[Response] Following the reviewer’s suggestion, we summarized the climatic, soil, and management characteristics of each validation site in Table S1 (see above) and described the broad pedoclimatic gradient represented by these sites in Section 2.3:
Lines 256-264: “The selected eight sites encompass a broad pedoclimatic and management gradient across European croplands (Table S1). They represent contrasting soil conditions, including Luvisol, Fluvisol, Cambisol, and Chernozem/Phaeozem, with the ranges of silt, clay and sand contents from 26.6 to 78.1 %, from 14.7 to 53.0 %, and from 7.2 to 38.0 %, respectively. These sites also cover diverse climatic conditions in air temperature, incoming shortwave solar radiation, precipitation and cloud cover at annual scale. Seasonal variability in solar radiation and soil moisture (characterized by the seasonal minimum and maximum values in Table S1) captures the range of environmental conditions experienced across seasons. In addition, these sites implement different agricultural management practices, including crop rotations of 2-8 years and bare soil periods occurring mainly in spring, autumn and winter (approximately 30-180 days, Fig. S2). ”
Q4: L238 please cite Figure 3
[Response] Following the reviewer’s suggestion, we cited Fig 3 in section 2.3:
Line 245-248: “Five types of αbs datasets were utilized to evaluate the performance of the final spatiotemporal albedo predictions (αbs, RF) at seven ICOS European cropland sites (BE-Lon, FR-Lam, FR-Aur, FR-EM2, DE-RuS, DE-Geb, DE-Kli), one European Fluxes Database Cluster (EFDC) European cropland site (CZ-KrP), and one Swedish cropland site in Uppsala, and the overall European cropland regions during bare soil periods (Fig. 3, Table 3).”
Q5: Figure 5 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3. Figure 6 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3
[Response] Following the reviewer’s suggestion, we adjusted the range of y-axis to 0-0.3 in Figures 5-6. The modified Figures 5-6 please see Supplement.
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AC1: 'Reply on RC1', Ke Yu, 28 Jul 2026
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EC1: 'Comment on egusphere-2026-219', Cornelius Senf, 14 Jul 2026
### We received a review report by an external reviewer, which I am posting on behal of the reviewer:
Comments to the manuscript (egusphere-2026-219) of Ke Yu, Yang Su, Philippe Ciais, Ronny Lauerwald, David Makowski, Tianqi Shi, Shengbiao Wu, Petra Sieber, Chuanlong Zhou, and Daniel Goll, entitled “ Spatiotemporal dynamics and drivers of bare soil albedo in European croplands”
The authors of this manuscript present a novel method to determine the spatiotemporal albedo variation for European cropland without vegetation cover or litter at field-scale. This method requires significant skill in processing satellite data and combining it with other cartographic data. The authors argue that albedo of bare arable fields, due to the large total area of cropland worldwide, can significantly influence the radiation flux between the atmosphere and the Earth's surface. First, the authors identified periods when cropland is bare and their albedo (abs) values were extracted from the 10-meter, 5-day Sentinel-2 reflectance products obtained from 2018-2020 after filtering clouds and snow cover using four spectral indices: NDVI, NBR2, GI, and NBR. The thresholds for these indices for extracting bare soils were established based on the reflectance spectra of over 8,000 soil samples from around the world. The Lucas 2018 Copernicus land dataset and 10-meter ESA WorldCover map were also used in this stage. Next, two random forest regression models were trained to determine during bare soil periods spacial distribution and temporal anomalies abs at the European cropland scale. This first model predicted the spatial distribution of the temporal average of abs, using the following input features: temporal-averaged local solar zenith angle (LSZA), soil organic carbon, sand and clay contents, bulk density, pH, temporally averaged soil moisture (SM) and elevation. However, this second model predicted the temporal variation of abs using as input features: LSZA, time of day, days of year, relative SM anomaly and the predicted values of spatial abs provided by the first model. A dataset containing almost 76,000 values of the temporal average of abs was used to train and test the first model, and a dataset containing almost 57,000 values of the temporal anomaly of abs was used to train and test the second model. Finally, the abs values predicted by these models were compared with existing datasets at local and European scales. Five types of abs datasets at seven European cropland sites from Belgium, France, Germany and the Czech Republic were used to evaluate the performance of the final spatiotemporal abs predictions for all European cropping regions during bare soil periods.
These models successfully reproduced both spatial patterns and temporal variability of abs across European arable lands, allowing the identification of bare soil factors that most strongly influence the variability of their abs values. It was found, as in previously published papers, that the spatial distribution of abs of bare soils depends primarily on LSZA, averaged SM and elevation, while their temporal variation depends on instantaneous SZA, and SM anomalies. My research also demonstrates that the short-term variability of bare soils abs depends on the soil surface roughness shaped by agricultural tools and its modification by heavy rainfall. The spatiotemporal average of abs, 0.15 ± 0.01 for European arable land in 2018–2020, is similar to its previously determined value of 0.15 to 0.22 obtained from over 7,500 topsoil samples across Europe in 2022. The multi-year spatial pattern established in this manuscript shows broad west-to-east and north-to-south gradients, corresponding to the main soil zones, with darker surfaces where organic matter content is higher and lighter surfaces where carbonates predominate. The Iberian Peninsula, southwestern and southern France, and the coastal regions of the Mediterranean are characterized by light soils with abs of 0.18-0.20. In contrast, soils in north-central and eastern Europe (e.g., Germany and Poland) are dark, with abs of 0.12-0.15. The authors of this manuscript have found that the prediction of daily abs values of bare soils also depends on the type of units classifying them. They report that the errors in prediction abs were smaller for bright Alfisols in Belgium and France than for dark Mollisols in Germany and Inceptisols in the Czech Republic. Therefore, in similar studies, the influence of soil classification should also be taken into account.
MAJOR COMMENT
In my opinion, the daily abs prediction for bare soils would be more accurate if the authors of this manuscript also included the roughness of the bare soils among their analyzed soil properties, resulting from the use of specific agricultural tools, modification of this roughness after heavy rainfall, and the formation of a thin crust, primarily on heavy soils. The omission of soil roughness in this study seems strange to me, especially since the authors mentioned this property as significantly affecting the albedo of bare soils in the "Introduction" (line 43).
MINOR COMMENTS
I have found a certain disagreement between references in the list of “References” and references inside of the manuscript text. The following items in the “References” list, Carrer et al. 2010, Jr et al. 2007, and Liu et al. 2002, are not referenced in the text. Also, several items mentioned in the text, Carrier et al. 2018, Charles et al. 2007, Liu et al. 2023, and Liu et al. 2002, are not referenced in the “References” list.
What the authors have presented in this manuscript significantly complements the existing numerical data on the spatiotemporal variation of daily albedo in European bare soils. Therefore, I recommend publication of this manuscript in the journal "Biogeosciences," taking into account only these minor comments. I believe that incorporating this major comment of mine into the revised manuscript will not be possible.Citation: https://doi.org/10.5194/egusphere-2026-219-EC1 -
AC2: 'Reply on EC1', Ke Yu, 28 Jul 2026
We thank the reviewer for their thorough and accurate summary of our methodology and findings. We also appreciate the reviewer highlighting the role of soil surface roughness and its modification by heavy rainfall as a contributor to short-term albedo variability, and the consistency of our derived European-scale average (0.15 ± 0.01) with previously published values. Regarding the influence of soil taxonomic class on prediction accuracy (better performance for bright Alfisols versus dark Mollisols/Inceptisols), we agree this is an important point and have ensured it is appropriately emphasized as a consideration for similar future studies (see response 1).
MAJOR COMMENT
Q1. In my opinion, the daily abs prediction for bare soils would be more accurate if the authors of this manuscript also included the roughness of the bare soils among their analyzed soil properties, resulting from the use of specific agricultural tools, modification of this roughness after heavy rainfall, and the formation of a thin crust, primarily on heavy soils. The omission of soil roughness in this study seems strange to me, especially since the authors mentioned this property as significantly affecting the albedo of bare soils in the "Introduction" (line 43).[Response] We agree that soil roughness, influenced by agriculture practices, rainfall events and crust formation, is an important determinant of bare soil albedo (Matthias et al., 2000; Cierniewski et al., 2023), however there is a lack of gridded soil roughness reconstructions required for the upscaling. We discussed the implication of the omission changes in soil roughness but admit it was rather limited (See section 4.3). Following the reviewer's suggestion, we now extended the discussions. In addition, we note that some selected predictors included in our RF framework, such as soil texture (sand and clay content) and bulk density, could partly capture the variability of soil roughness (Herodowicz-Mleczak et al., 2022; Rizzo et al., 2023), but the quick change of roughness induced by agriculture practices, rainfall events and crust formation cannot be resolved. We have clarified the missed soil roughness in section 4.3.
“One important example is varying soil surface roughness which varies according to agriculture practices, rainfall events and crust formation. Its impact on αbs was not explicitly represented as the required spatiotemporal datasets of soil roughness are currently unavailable at the European scale. In addition, assessing the impact of temporal variation of soil roughness on predicted αbs at the site scale remains challenging, as no soil roughness measurements are available at the evaluation sites. Our RF framework relies on intrinsic soil properties that may partially capture roughness-related variability (Herodowicz-Mleczak et al., 2022; Rizzo et al., 2023) but cannot explicitly resolve the effects of agricultural practices, rainfall-induced smoothing, or soil crust formation.”
MINOR COMMENTS
Q2: I have found a certain disagreement between references in the list of “References” and references inside of the manuscript text. The following items in the “References” list, Carrer et al. 2010, Jr et al. 2007, and Liu et al. 2002, are not referenced in the text. Also, several items mentioned in the text, Carrier et al. 2018, Charles et al. 2007, Liu et al. 2023, and Liu et al. 2002, are not referenced in the “References” list.[Response] We apologise for the misleading information in the citations and reference list. We have carefully checked all references and made the following corrections:
- The unreferenced entries ‘Carrer et al. 2010, Jr et al. 2007, and Liu et al. 2002’ have been removed from the reference list.
- ‘Carrier et al. 2018’ has been added to the reference list.
- The citation ‘Charles et al. 2007’ in the manuscript was a typographical error and has been corrected to Garten et al., 2007, which has also been included in the reference list.
- The citation "Liu et al. (2023)" referred to Lin et al. (2022) in Section 2.1.1. This has been corrected accordingly.Citation: https://doi.org/10.5194/egusphere-2026-219-AC2
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AC2: 'Reply on EC1', Ke Yu, 28 Jul 2026
Peer review completion
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2026-219', Anonymous Referee #1, 27 Mar 2026
The manuscript “Spatiotemporal dynamics and drivers of bare soil albedo in European croplands” investigates the spatial patterns and temporal dynamics of bare soil albedo in European croplands. For this study authors reconstructed field-scale, spatiotemporally continuous bare soil albedo at a 5-day temporal resolution and 0.3 km spatial resolution using Sentinel-2 reflectance observations. Bare soil periods were identified by multiple spectral indices and the corresponding soil albedo values are derived for the period 2018-2020 using a novel machine learning framework. Two hypotheses were tested, i) spatial distribution of bare soil albedo, soil properties exert a stronger control than radiative factors, and (ii) for temporal variability of bare soil albedo, fluctuation in soil moisture is more influential than static soil properties.
To address these hypotheses, the study quantified the spatial patterns of bare soil albedo at the field scale across Europe, characterized the temporal dynamics of bare soil albedo during bare soil periods, and identified the relative contributions of soil and topographic properties. This was done by random forest models to separately capture the long-term spatial structure and short-term temporal anomalies of bare soil albedo.
Model evaluation showed that the estimated bare soil albedo reproduces observed spatial gradients and seasonal variability across European croplands. Variations in bare soil albedo were controlled by soil properties, and short-term soil moisture dynamics rather than by any single factor. Variations in bare soil albedo were comparable to those related management solutions. Authors recommend to better considered bare soil albedo in order to improve the representation of surface radiative properties and land-atmosphere coupling in agroecosystem and land surface models.
I have read this manuscript with interest. The study is well written, clearly illustrated, and easy to follow.
It convincingly highlights the importance of incorporating daily bare soil albedo information into land surface and agroecosystem models in order to improve the simulation of short-term thermal and moisture dynamics. This is particularly relevant, as static or climatological representations of bare soil albedo may limit the ability of models to capture rapid transitions associated with soil wetting-drying cycles.
Overall, I the manuscript is of very good quality and can be accepted after some minor revisions.
General comments
I feel that the manuscript would benefit from a clearer and more explicit illustration of the relationships between soil moisture, soil texture, and albedo, which are mentioned in the abstract, objectives, and discussion (see also L391, L429, L455 and following).
As a 1st step I this suggest moving Supplementary Figure 7 (linear relationship between site-level bare soil albedo and soil moisture) into the main text, as it directly supports some of the key arguments of the study.
Along the same lines, it would be useful to provide some basic contextual information for the validation sites, such as pedoclimatic conditions, crop rotations, and fallow periods. This would help the reader better understand the representativeness and variability of the dataset.
Specific comments
L238 and following:
Could you add a notion of a gradient in your evaluation dataset? The dataset includes only eight sites, and it would strengthen the manuscript to better demonstrate that these sites cover a meaningful pedoclimatic gradient. I recommend to better describ how the selected sites represent variability in:
soil types (e.g. texture, possibly linked to soil color),
climatic conditions (e.g. radiation, cloud cover),
management practices (e.g. crop rotations, fallow periods),
seasonal dynamics (as proxies for variations in radiation intensity, cloud cover, and soil moisture throughout the year; see also L271).
L238 please cite Figure 3
Figure 5 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3
Figure 6 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3
Citation: https://doi.org/10.5194/egusphere-2026-219-RC1 -
AC1: 'Reply on RC1', Ke Yu, 28 Jul 2026
[Response] We thank the reviewer for their positive assessment and are grateful for their encouraging comments on the quality, clarity, and relevance of our study. Please find our point-by-point responses to the reviewer’s comments below
Q1：I feel that the manuscript would benefit from a clearer and more explicit illustration of the relationships between soil moisture, soil texture, and albedo, which are mentioned in the abstract, objectives, and discussion (see also L391, L429, L455 and following).
[Response] Following the reviewer’s suggestion, we have revised Introduction and section 4.2 to clarify the relationship between soil moisture, soil texture, and albedo.
Lines 44-49: “Bare soil albedo is controlled by a combination of intrinsic soil properties and varying environmental conditions (He et al., 2019). Soil properties, including texture, mineral composition, roughness, organic matter content, and bulk density determine the optical characteristics of soil surface (Matthias et al., 2000, Cierniewski et al., 2018) and soil hydrological properties (Han et al., 2023). As a result, they affect soil albedo directly and indirectly (via modulating the relationship between albedo and water availability). Soil moisture and solar radiation geometry further modulate the short-term temporal variability of soil albedo by altering the amount and direction of the observed reflected solar radiation (Hapke, 1981; Gascoin et al., 2009, Badía et al., 2016).”
Lines 380-384: “The decomposition of bare soil albedo into spatial and temporal components provides a framework for interpreting its physical controls. Intrinsic soil properties affect bare soil albedo by establishing the optical characteristics of the soil surface and regulating soil-water interaction, while soil moisture dynamics and radiative geometry control the temporal fluctuations of the observed bare soil albedo.”
Lines 399-401: “The magnitude and duration of these responses depend on the soil water retention capacity (Post et al., 2000), resulting in different αbs responses to the same soil moisture change for different soil textures”.
Lines 408-413: “Soil properties explain much of the stable spatial structure onto which moisture and geometry act (Rizzo et al., 2023). For example, soil texture influences bare soil albedo through both direct and indirect pathways. Particle size, mineral composition directly influence soil optical properties (e.g., surface color and scattering characteristics), thereby establishing the inherent bare soil albedo. In addition, these properties related with texture regulate the soil hydraulic behaviour (Sadeghi et al 2018)”
Lines 430-432: “Overall, these controls are interacting components governing bare soil albedo rather than independent drivers. Among the intrinsic soil properties, soil texture plays an important role because it establishes the background optical characteristics of the soil surface and regulates soil-water interactions, while soil moisture and solar geometry determine the short-term variability of the observed αbs.”
Q2: As a 1st step I suggest moving Supplementary Figure 7 (linear relationship between site-level bare soil albedo and soil moisture) into the main text, as it directly supports some of the key arguments of the study.
Along the same lines, it would be useful to provide some basic contextual information for the validation sites, such as pedoclimatic conditions, crop rotations, and fallow periods. This would help the reader better understand the representativeness and variability of the dataset.[Response] Following the reviewer’s suggestion, we moved Fig S7 into section 3.3. We also added a summary table (Table S1) describing the validation sites, including their climatic conditions, dominant crop rotations, and typical length of fallow periods. Regarding fallow periods, we note that quantifying a fixed period for each site is difficult, as it varies among years depending on crop rotation and management practices (e.g., cover crops and tillage operations). Therefore, we provide the average fallow period duration in 2018-2020 estimated from the crop calendars reported for each site. Table S1 please see the Supplement.
Specific comments
Q3. L238 and following:
Could you add a notion of a gradient in your evaluation dataset? The dataset includes only eight sites, and it would strengthen the manuscript to better demonstrate that these sites cover a meaningful pedoclimatic gradient. I recommend to better describ how the selected sites represent variability in:
soil types (e.g. texture, possibly linked to soil color), climatic conditions (e.g. radiation, cloud cover), management practices (e.g. crop rotations, fallow periods), seasonal dynamics (as proxies for variations in radiation intensity, cloud cover, and soil moisture throughout the year; see also L271).[Response] Following the reviewer’s suggestion, we summarized the climatic, soil, and management characteristics of each validation site in Table S1 (see above) and described the broad pedoclimatic gradient represented by these sites in Section 2.3:
Lines 256-264: “The selected eight sites encompass a broad pedoclimatic and management gradient across European croplands (Table S1). They represent contrasting soil conditions, including Luvisol, Fluvisol, Cambisol, and Chernozem/Phaeozem, with the ranges of silt, clay and sand contents from 26.6 to 78.1 %, from 14.7 to 53.0 %, and from 7.2 to 38.0 %, respectively. These sites also cover diverse climatic conditions in air temperature, incoming shortwave solar radiation, precipitation and cloud cover at annual scale. Seasonal variability in solar radiation and soil moisture (characterized by the seasonal minimum and maximum values in Table S1) captures the range of environmental conditions experienced across seasons. In addition, these sites implement different agricultural management practices, including crop rotations of 2-8 years and bare soil periods occurring mainly in spring, autumn and winter (approximately 30-180 days, Fig. S2). ”
Q4: L238 please cite Figure 3
[Response] Following the reviewer’s suggestion, we cited Fig 3 in section 2.3:
Line 245-248: “Five types of αbs datasets were utilized to evaluate the performance of the final spatiotemporal albedo predictions (αbs, RF) at seven ICOS European cropland sites (BE-Lon, FR-Lam, FR-Aur, FR-EM2, DE-RuS, DE-Geb, DE-Kli), one European Fluxes Database Cluster (EFDC) European cropland site (CZ-KrP), and one Swedish cropland site in Uppsala, and the overall European cropland regions during bare soil periods (Fig. 3, Table 3).”
Q5: Figure 5 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3. Figure 6 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3
[Response] Following the reviewer’s suggestion, we adjusted the range of y-axis to 0-0.3 in Figures 5-6. The modified Figures 5-6 please see Supplement.
-
AC1: 'Reply on RC1', Ke Yu, 28 Jul 2026
-
EC1: 'Comment on egusphere-2026-219', Cornelius Senf, 14 Jul 2026
### We received a review report by an external reviewer, which I am posting on behal of the reviewer:
Comments to the manuscript (egusphere-2026-219) of Ke Yu, Yang Su, Philippe Ciais, Ronny Lauerwald, David Makowski, Tianqi Shi, Shengbiao Wu, Petra Sieber, Chuanlong Zhou, and Daniel Goll, entitled “ Spatiotemporal dynamics and drivers of bare soil albedo in European croplands”
The authors of this manuscript present a novel method to determine the spatiotemporal albedo variation for European cropland without vegetation cover or litter at field-scale. This method requires significant skill in processing satellite data and combining it with other cartographic data. The authors argue that albedo of bare arable fields, due to the large total area of cropland worldwide, can significantly influence the radiation flux between the atmosphere and the Earth's surface. First, the authors identified periods when cropland is bare and their albedo (abs) values were extracted from the 10-meter, 5-day Sentinel-2 reflectance products obtained from 2018-2020 after filtering clouds and snow cover using four spectral indices: NDVI, NBR2, GI, and NBR. The thresholds for these indices for extracting bare soils were established based on the reflectance spectra of over 8,000 soil samples from around the world. The Lucas 2018 Copernicus land dataset and 10-meter ESA WorldCover map were also used in this stage. Next, two random forest regression models were trained to determine during bare soil periods spacial distribution and temporal anomalies abs at the European cropland scale. This first model predicted the spatial distribution of the temporal average of abs, using the following input features: temporal-averaged local solar zenith angle (LSZA), soil organic carbon, sand and clay contents, bulk density, pH, temporally averaged soil moisture (SM) and elevation. However, this second model predicted the temporal variation of abs using as input features: LSZA, time of day, days of year, relative SM anomaly and the predicted values of spatial abs provided by the first model. A dataset containing almost 76,000 values of the temporal average of abs was used to train and test the first model, and a dataset containing almost 57,000 values of the temporal anomaly of abs was used to train and test the second model. Finally, the abs values predicted by these models were compared with existing datasets at local and European scales. Five types of abs datasets at seven European cropland sites from Belgium, France, Germany and the Czech Republic were used to evaluate the performance of the final spatiotemporal abs predictions for all European cropping regions during bare soil periods.
These models successfully reproduced both spatial patterns and temporal variability of abs across European arable lands, allowing the identification of bare soil factors that most strongly influence the variability of their abs values. It was found, as in previously published papers, that the spatial distribution of abs of bare soils depends primarily on LSZA, averaged SM and elevation, while their temporal variation depends on instantaneous SZA, and SM anomalies. My research also demonstrates that the short-term variability of bare soils abs depends on the soil surface roughness shaped by agricultural tools and its modification by heavy rainfall. The spatiotemporal average of abs, 0.15 ± 0.01 for European arable land in 2018–2020, is similar to its previously determined value of 0.15 to 0.22 obtained from over 7,500 topsoil samples across Europe in 2022. The multi-year spatial pattern established in this manuscript shows broad west-to-east and north-to-south gradients, corresponding to the main soil zones, with darker surfaces where organic matter content is higher and lighter surfaces where carbonates predominate. The Iberian Peninsula, southwestern and southern France, and the coastal regions of the Mediterranean are characterized by light soils with abs of 0.18-0.20. In contrast, soils in north-central and eastern Europe (e.g., Germany and Poland) are dark, with abs of 0.12-0.15. The authors of this manuscript have found that the prediction of daily abs values of bare soils also depends on the type of units classifying them. They report that the errors in prediction abs were smaller for bright Alfisols in Belgium and France than for dark Mollisols in Germany and Inceptisols in the Czech Republic. Therefore, in similar studies, the influence of soil classification should also be taken into account.
MAJOR COMMENT
In my opinion, the daily abs prediction for bare soils would be more accurate if the authors of this manuscript also included the roughness of the bare soils among their analyzed soil properties, resulting from the use of specific agricultural tools, modification of this roughness after heavy rainfall, and the formation of a thin crust, primarily on heavy soils. The omission of soil roughness in this study seems strange to me, especially since the authors mentioned this property as significantly affecting the albedo of bare soils in the "Introduction" (line 43).
MINOR COMMENTS
I have found a certain disagreement between references in the list of “References” and references inside of the manuscript text. The following items in the “References” list, Carrer et al. 2010, Jr et al. 2007, and Liu et al. 2002, are not referenced in the text. Also, several items mentioned in the text, Carrier et al. 2018, Charles et al. 2007, Liu et al. 2023, and Liu et al. 2002, are not referenced in the “References” list.
What the authors have presented in this manuscript significantly complements the existing numerical data on the spatiotemporal variation of daily albedo in European bare soils. Therefore, I recommend publication of this manuscript in the journal "Biogeosciences," taking into account only these minor comments. I believe that incorporating this major comment of mine into the revised manuscript will not be possible.Citation: https://doi.org/10.5194/egusphere-2026-219-EC1 -
AC2: 'Reply on EC1', Ke Yu, 28 Jul 2026
We thank the reviewer for their thorough and accurate summary of our methodology and findings. We also appreciate the reviewer highlighting the role of soil surface roughness and its modification by heavy rainfall as a contributor to short-term albedo variability, and the consistency of our derived European-scale average (0.15 ± 0.01) with previously published values. Regarding the influence of soil taxonomic class on prediction accuracy (better performance for bright Alfisols versus dark Mollisols/Inceptisols), we agree this is an important point and have ensured it is appropriately emphasized as a consideration for similar future studies (see response 1).
MAJOR COMMENT
Q1. In my opinion, the daily abs prediction for bare soils would be more accurate if the authors of this manuscript also included the roughness of the bare soils among their analyzed soil properties, resulting from the use of specific agricultural tools, modification of this roughness after heavy rainfall, and the formation of a thin crust, primarily on heavy soils. The omission of soil roughness in this study seems strange to me, especially since the authors mentioned this property as significantly affecting the albedo of bare soils in the "Introduction" (line 43).[Response] We agree that soil roughness, influenced by agriculture practices, rainfall events and crust formation, is an important determinant of bare soil albedo (Matthias et al., 2000; Cierniewski et al., 2023), however there is a lack of gridded soil roughness reconstructions required for the upscaling. We discussed the implication of the omission changes in soil roughness but admit it was rather limited (See section 4.3). Following the reviewer's suggestion, we now extended the discussions. In addition, we note that some selected predictors included in our RF framework, such as soil texture (sand and clay content) and bulk density, could partly capture the variability of soil roughness (Herodowicz-Mleczak et al., 2022; Rizzo et al., 2023), but the quick change of roughness induced by agriculture practices, rainfall events and crust formation cannot be resolved. We have clarified the missed soil roughness in section 4.3.
“One important example is varying soil surface roughness which varies according to agriculture practices, rainfall events and crust formation. Its impact on αbs was not explicitly represented as the required spatiotemporal datasets of soil roughness are currently unavailable at the European scale. In addition, assessing the impact of temporal variation of soil roughness on predicted αbs at the site scale remains challenging, as no soil roughness measurements are available at the evaluation sites. Our RF framework relies on intrinsic soil properties that may partially capture roughness-related variability (Herodowicz-Mleczak et al., 2022; Rizzo et al., 2023) but cannot explicitly resolve the effects of agricultural practices, rainfall-induced smoothing, or soil crust formation.”
MINOR COMMENTS
Q2: I have found a certain disagreement between references in the list of “References” and references inside of the manuscript text. The following items in the “References” list, Carrer et al. 2010, Jr et al. 2007, and Liu et al. 2002, are not referenced in the text. Also, several items mentioned in the text, Carrier et al. 2018, Charles et al. 2007, Liu et al. 2023, and Liu et al. 2002, are not referenced in the “References” list.[Response] We apologise for the misleading information in the citations and reference list. We have carefully checked all references and made the following corrections:
- The unreferenced entries ‘Carrer et al. 2010, Jr et al. 2007, and Liu et al. 2002’ have been removed from the reference list.
- ‘Carrier et al. 2018’ has been added to the reference list.
- The citation ‘Charles et al. 2007’ in the manuscript was a typographical error and has been corrected to Garten et al., 2007, which has also been included in the reference list.
- The citation "Liu et al. (2023)" referred to Lin et al. (2022) in Section 2.1.1. This has been corrected accordingly.Citation: https://doi.org/10.5194/egusphere-2026-219-AC2
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AC2: 'Reply on EC1', Ke Yu, 28 Jul 2026
Peer review completion
Journal article(s) based on this preprint
Data sets
Bare soil albedo datasets at high spatio-temporal resolution from Sentinel-2 observations Ke Yu, Yang Su, Philippe Ciais, Ronny Lauerwald, David Makowski, Tianqi Shi, Shengbiao Wu, Petra Sieber, Chuanlong Zhou, Daniel S. Goll https://doi.org/10.6084/m9.figshare.30488027
Model code and software
code for producing European bare soil albedo Ke Yu, Yang Su, Philippe Ciais, Ronny Lauerwald, David Makowski, Tianqi Shi, Shengbiao Wu, Petra Sieber, Chuanlong Zhou, Daniel S. Goll https://doi.org/10.6084/m9.figshare.30488027
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Philippe Ciais
Ronny Lauerwald
David Makowski
Tianqi Shi
Shengbiao Wu
Petra Sieber
Chuanlong Zhou
Daniel Goll
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The manuscript “Spatiotemporal dynamics and drivers of bare soil albedo in European croplands” investigates the spatial patterns and temporal dynamics of bare soil albedo in European croplands. For this study authors reconstructed field-scale, spatiotemporally continuous bare soil albedo at a 5-day temporal resolution and 0.3 km spatial resolution using Sentinel-2 reflectance observations. Bare soil periods were identified by multiple spectral indices and the corresponding soil albedo values are derived for the period 2018-2020 using a novel machine learning framework. Two hypotheses were tested, i) spatial distribution of bare soil albedo, soil properties exert a stronger control than radiative factors, and (ii) for temporal variability of bare soil albedo, fluctuation in soil moisture is more influential than static soil properties.
To address these hypotheses, the study quantified the spatial patterns of bare soil albedo at the field scale across Europe, characterized the temporal dynamics of bare soil albedo during bare soil periods, and identified the relative contributions of soil and topographic properties. This was done by random forest models to separately capture the long-term spatial structure and short-term temporal anomalies of bare soil albedo.
Model evaluation showed that the estimated bare soil albedo reproduces observed spatial gradients and seasonal variability across European croplands. Variations in bare soil albedo were controlled by soil properties, and short-term soil moisture dynamics rather than by any single factor. Variations in bare soil albedo were comparable to those related management solutions. Authors recommend to better considered bare soil albedo in order to improve the representation of surface radiative properties and land-atmosphere coupling in agroecosystem and land surface models.
I have read this manuscript with interest. The study is well written, clearly illustrated, and easy to follow.
It convincingly highlights the importance of incorporating daily bare soil albedo information into land surface and agroecosystem models in order to improve the simulation of short-term thermal and moisture dynamics. This is particularly relevant, as static or climatological representations of bare soil albedo may limit the ability of models to capture rapid transitions associated with soil wetting-drying cycles.
Overall, I the manuscript is of very good quality and can be accepted after some minor revisions.
General comments
I feel that the manuscript would benefit from a clearer and more explicit illustration of the relationships between soil moisture, soil texture, and albedo, which are mentioned in the abstract, objectives, and discussion (see also L391, L429, L455 and following).
As a 1st step I this suggest moving Supplementary Figure 7 (linear relationship between site-level bare soil albedo and soil moisture) into the main text, as it directly supports some of the key arguments of the study.
Along the same lines, it would be useful to provide some basic contextual information for the validation sites, such as pedoclimatic conditions, crop rotations, and fallow periods. This would help the reader better understand the representativeness and variability of the dataset.
Specific comments
L238 and following:
Could you add a notion of a gradient in your evaluation dataset? The dataset includes only eight sites, and it would strengthen the manuscript to better demonstrate that these sites cover a meaningful pedoclimatic gradient. I recommend to better describ how the selected sites represent variability in:
soil types (e.g. texture, possibly linked to soil color),
climatic conditions (e.g. radiation, cloud cover),
management practices (e.g. crop rotations, fallow periods),
seasonal dynamics (as proxies for variations in radiation intensity, cloud cover, and soil moisture throughout the year; see also L271).
L238 please cite Figure 3
Figure 5 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3
Figure 6 as non of the values is over 0.4 may be adjust the Figure to y axis 0-0.3